Various analysis techniques have been developed to understand and mitigate the impact of stellar activity effects. The early efforts include decorrelation against proxy indicators of activity, or simply rejecting a planet candidate if the same periodicity was found in the FWHM (Full Width Half Maximum), BIS (Bisector Inverse Slope)
or in the chromospheric Ca II H&K emission flux, as in the RV [1]}. [2]} used well-sampled light curves to predict the stellar variability expected in RV measurements. Known as the FF\(^\prime \)  method, this approach uses the light curve and its first derivative to approximate the effects of spots and faculae, and requires no information about the rotation period or spot modelling. It does, however, require simultaneous high-precision photometry.
Recently, Gaussian Process regression has been used to model the correlated noise induced by stellar activity in the RV signal, by fitting the orbital signal simultaneously [3]}. Although GP regression with a quasi-periodic kernel is successful at fitting rotationally modulated activity, other signals are also present.
